Abstract:Background: The rapid advancement of large language models (LLMs) has given rise to AI-native applications, a new paradigm in software engineering that fundamentally redefines how software is designed, developed, and evolved. Despite their growing prominence, AI-native applications still lack a unified engineering definition and architectural blueprint, leaving practitioners without systematic guidance for system design, quality assurance, and technology selection.
Objective: This study seeks to establish a comprehensive understanding of AI-native applications by identifying their defining characteristics, key quality attributes, and typical technology stacks, as well as by clarifying the opportunities and challenges they present.
Method: We conducted a grey literature review, integrating conceptual perspectives retrieved from targeted Google and Bing searches with practical insights derived from leading open-source projects on GitHub. A structured protocol encompassing source selection, quality assessment, and thematic analysis was applied to synthesize findings across heterogeneous sources.
Results: We finally identified 106 studies based on the selection criteria. The analysis reveals that AI-native applications are distinguished by two core pillars: the central role of AI as the system's intelligence paradigm and their inherently probabilistic, non-deterministic nature. Critical quality attributes include reliability, usability, performance efficiency, and AI-specific observability. In addition, a typical technology stack has begun to emerge, comprising LLM orchestration frameworks, vector databases, and AI-native observability platforms. These systems emphasize response quality, cost-effectiveness, and outcome predictability, setting them apart from conventional software systems.
Conclusion: This study is the first to propose a dual-layered engineering blueprint...
From: Lingli Cao [view email]
[v1]
Tue, 16 Sep 2025 15:01:23 UTC (3,768 KB)
[v2]
Tue, 21 Jul 2026 08:34:13 UTC (1,080 KB)
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